1. Field of the Invention
The present invention relates generally to image recognition and more specifically using Support Vector Machine classifiers for face detection and recognition.
2. Background of the Invention
Face detection and recognition systems have various applications such as intelligent human-computer interfaces, surveillance and monitoring systems, and content-based image search and retrieval using a face. However, previous attempts to develop efficient face detection and recognition systems were not successful because the computers and algorithms used in conventional face detection and recognition systems could not effectively handle the large amount of data and complicated computation inherently involved in a face recognition system.
Face detection has direct relevance to a face recognition system, because the first important step of face recognition is to define and locate faces in an arbitrary image. Several different techniques have been applied to the problem of face detection. These techniques include Neural Networks, detection of face features using geometrical constraints, density estimation of the training data, labeled graphs and clustering and distribution-based modeling. In general, face detection and recognition techniques are based on two main approaches, feature-based approaches and template-based approaches. In feature-based approaches, recognition is based on the detection of facial features, such as the eyes, nose, mouth corners, etc. The image analysis around these features and geometrical properties between these features are used in recognition. However, current methods for facial feature extraction are often not robust and exact enough to satisfy the demand of face recognition applications.
Template-based approaches generally represent faces directly by gray level image features or its transformation forms. Recognition methods can be based on similarity criteria such as minimum distance classification or Fisher's discriminant analysis. Recently, Support Vector Machine (SVM) techniques have been used in template-based approaches.
There exists a need for a more efficient face detection and recognition system for practical applications that requires less computational power.